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import numpy as np |
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from .data.image import Image, LabelMap |
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from .data.subject import Subject |
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from .transforms.preprocessing.spatial.to_canonical import ToCanonical |
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def import_mpl_plt(): |
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try: |
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import matplotlib as mpl |
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import matplotlib.pyplot as plt |
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except ImportError as e: |
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raise ImportError('Install matplotlib for plotting support') from e |
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return mpl, plt |
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def rotate(image, radiological=True): |
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# Rotate for visualization purposes |
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image = np.rot90(image, -1) |
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if radiological: |
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image = np.fliplr(image) |
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return image |
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def plot_volume( |
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image: Image, |
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radiological=True, |
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channel=-1, # default to foreground for binary maps |
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axes=None, |
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cmap=None, |
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output_path=None, |
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show=True, |
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xlabels=True, |
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): |
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_, plt = import_mpl_plt() |
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fig = None |
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if axes is None: |
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fig, axes = plt.subplots(1, 3) |
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sag_axis, cor_axis, axi_axis = axes |
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image = ToCanonical()(image) |
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data = image.data[channel] |
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indices = np.array(data.shape) // 2 |
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i, j, k = indices |
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slice_x = rotate(data[i, :, :], radiological=radiological) |
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slice_y = rotate(data[:, j, :], radiological=radiological) |
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slice_z = rotate(data[:, :, k], radiological=radiological) |
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kwargs = {} |
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is_label = isinstance(image, LabelMap) |
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if isinstance(cmap, dict): |
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slices = slice_x, slice_y, slice_z |
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slice_x, slice_y, slice_z = color_labels(slices, cmap) |
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else: |
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if cmap is None: |
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cmap = 'cubehelix' if is_label else 'gray' |
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kwargs['cmap'] = cmap |
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if is_label: |
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kwargs['interpolation'] = 'none' |
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sr, sa, ss = image.spacing |
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kwargs['origin'] = 'lower' |
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sag_aspect = ss / sa |
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sag_axis.imshow(slice_x, aspect=sag_aspect, **kwargs) |
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if xlabels: |
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sag_axis.set_xlabel('A') |
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sag_axis.set_ylabel('S') |
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sag_axis.invert_xaxis() |
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sag_axis.set_title('Sagittal') |
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cor_aspect = ss / sr |
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cor_axis.imshow(slice_y, aspect=cor_aspect, **kwargs) |
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if xlabels: |
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cor_axis.set_xlabel('R') |
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cor_axis.set_ylabel('S') |
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cor_axis.invert_xaxis() |
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cor_axis.set_title('Coronal') |
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axi_aspect = sa / sr |
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axi_axis.imshow(slice_z, aspect=axi_aspect, **kwargs) |
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if xlabels: |
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axi_axis.set_xlabel('R') |
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axi_axis.set_ylabel('A') |
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axi_axis.invert_xaxis() |
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axi_axis.set_title('Axial') |
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plt.tight_layout() |
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if output_path is not None and fig is not None: |
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fig.savefig(output_path) |
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if show: |
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plt.show() |
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def plot_subject( |
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subject: Subject, |
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cmap_dict=None, |
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show=True, |
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output_path=None, |
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figsize=None, |
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clear_axes=True, |
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**kwargs, |
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): |
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_, plt = import_mpl_plt() |
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subplots_kwargs = {'figsize': figsize} |
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try: |
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if clear_axes: |
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subject.check_consistent_spatial_shape() |
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subplots_kwargs['sharex'] = 'col' |
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subplots_kwargs['sharey'] = 'col' |
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except RuntimeError: # different shapes in subject |
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pass |
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fig, axes = plt.subplots(len(subject), 3, **subplots_kwargs) |
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# The array of axes must be 2D so that it can be indexed correctly within |
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# the plot_volume() function |
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axes = axes.reshape(-1, 3) |
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iterable = enumerate(subject.get_images_dict(intensity_only=False).items()) |
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axes_names = 'sagittal', 'coronal', 'axial' |
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for row_index, (name, image) in iterable: |
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row_axes = axes[row_index] |
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cmap = None |
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if cmap_dict is not None and name in cmap_dict: |
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cmap = cmap_dict[name] |
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last_row = row_index == len(axes) - 1 |
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plot_volume( |
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image, |
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axes=row_axes, |
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show=False, |
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cmap=cmap, |
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xlabels=last_row, |
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**kwargs, |
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) |
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for axis, axis_name in zip(row_axes, axes_names): |
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axis.set_title(f'{name} ({axis_name})') |
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plt.tight_layout() |
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if output_path is not None: |
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fig.savefig(output_path) |
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if show: |
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plt.show() |
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def color_labels(arrays, cmap_dict): |
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results = [] |
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for array in arrays: |
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si, sj = array.shape |
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rgb = np.zeros((si, sj, 3), dtype=np.uint8) |
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for label, color in cmap_dict.items(): |
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if isinstance(color, str): |
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mpl, _ = import_mpl_plt() |
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color = mpl.colors.to_rgb(color) |
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color = [255 * n for n in color] |
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rgb[array == label] = color |
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results.append(rgb) |
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return results |
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fepegar /
torchio
